The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Feature Selection Via Mathematical Programming
INFORMS Journal on Computing
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Artificial neural network approach for fault detection in rotary system
Applied Soft Computing
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
One-class support vector machines-an application in machine fault detection and classification
Computers and Industrial Engineering
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
International Journal of Data Analysis Techniques and Strategies
An adaptive artificial immune system for fault classification
Journal of Intelligent Manufacturing
Hi-index | 0.00 |
Support vector machine (SVM) is a classification method based on the structured risk minimization principle. Penalize, C; and kernel, @s parameters of SVM must be carefully selected in establishing an efficient SVM model. These parameters are selected by trial and error or man's experience. Artificial immune system (AIS) can be defined as a soft computing method inspired by theoretical immune system in order to solve science and engineering problems. A multi-objective artificial immune algorithm has been used to optimize the kernel and penalize parameters of SVM in this paper. In training stage of SVM, multiple solutions are found by using multi-objective artificial immune algorithm and then these parameters are evaluated in test stage. The proposed algorithm is applied to fault diagnosis of induction motors and anomaly detection problems and successful results are obtained.